1. Field of the Invention
This invention is related to the use of well data and seismic data to predict subsurface lithology.
2. Description of Related Art
For many years seismic exploration for oil and gas has been conducted by use of a source of seismic energy and the reception of the energy generated by the source by an array of seismic detectors. On land, the source of seismic energy may be a high explosive charge or another energy source having the capacity to deliver a series of impacts or mechanical vibrations to the earth's surface. Acoustic waves generated by these sources travel downwardly into the earth's subsurface and are reflected back from strata boundaries and reach the surface of the earth at varying intervals of time depending on the distance traveled and the characteristics of the subsurface traversed. These returning waves are detected by the sensors which function to transduce such acoustic waves into representative electrical signals. The detected signals are recorded for later processing using digital computers. Typically an array of sensors is laid out along a line to form a series of detection locations. More recently, seismic surveys are conducted with sensors and sources laid out in generally rectangular grids covering an area of interest, rather than along a single line, to enable construction of three dimensional views of reflector positions over wide areas. Normally, signals from sensors located at varying distances from the source are added together during processing to produce “stacked” seismic traces. In marine seismic surveys, the source of seismic energy is typically air guns. Marine seismic surveys typically employ a plurality of sources and/or a plurality of streamer cables, in which seismic sensors are mounted, to gather three dimensional data.
In 1979, Taner et al. published the work “Complex Seismic Trace Analysis”, Geophysics, Volume 44, pp. 1041–1063, and exploration geophysicists have subsequently developed a plurality of time-series transformations of seismic traces to obtain a variety of characteristics that describe the traces, which are generally referred to as “attributes”. Attributes may be computed prestack or poststack. Poststack attributes include reflection intensity, instantaneous frequency, reflection heterogeneity, acoustic impedance, velocity, dip, depth and azimuth. Prestack attributes include moveout parameters such as amplitude-versus-offset (AVO), and interval and average velocities.
It has been observed that specific seismic attributes are related to specific subsurface properties. For example, acoustic impedance may be related to porosity. Other subsurface properties appear to be related to other seismic attributes, but it may be unclear what the relationship is, as local factors may affect the data in unexpected ways.
Frequently, both well logging data and seismic data are available for a region of the earth which includes a subsurface region of interest. Core data may also be available. Typically, the well log data and, if available, the core data, are utilized for constructing a detailed log of subsurface properties at the location of the well bore. The seismic data, which include data gathered in the interwell region of interest, are then utilized to estimate the structure of the subsurface formation extending between well locations. Subsurface formation property mapping, however, is typically based solely on the wireline log and core sample data. More recently however, a number of proposals have been made for using seismic data gathered from the interwell region to improve the estimation of formation properties in the interwell region. See for example, U.S. Pat. Nos. 5,444,619; 5,691,958; 5,706,194; 5,940,777 and 5,828,981.
The past few years has seen the introduction of several methods which attempt to classify surface seismic information via the use of artificial neural networks. Some of these methods also use borehole data to further constrain this classification. See, for example, U.S. Pat. Nos. 5,444,619; 5,691,958; 5,706,194; 5,940,777 and 5,706,194.
B. Russell, D. Hampson,. J. Schvelke et al. & J. Quirein, Multiattribute Seismic Analysis, The Leading Edge, October, 1997, pp 1439–1443 describes a method for seismic analysis which makes use of artificial neural networks (ANN) to predict log-curves from multiple sets of seismic attributes.
A method for training a neural network using model-driven seismic attributes was presented in J. Walls, N. Derzhi, D. Dumas, T. Guidish, M. Taner and G. Taylor, North Sea Reservoir Characteistics using Rock Physics, Seismic Attributes, and Neural Networks: A Case History, Annual Meeting Abstracts, Society of Exploration Geophysicists, pp. 1572–1575 (1999). This trained network is then applied to surface seismic for lithology classification.
M. Morice, N. Keskes and F. Jganjean, F., Manual and Automatic Seismic Facies Analysis on SISMAGE™ Workstation, Annual Meeting Abstracts, Society of Exploration Geophysicists, p 320–323 (1996) describe a method for using Kohonen self organizing maps for seismic facies analysis of seismic data.
It was disclosed in M. Taner, Kohonen's Self Organizing Networks with “Conscience”, published on the Internet at http://www.rocksolidimages.com in 1997 that Kohonen self organizing maps allow for the classification of seismic data based upon the discriminating ability of one or more sets of representative derived attributes. Although Kohonen self organizing maps have been found to be effective tools for defining seismic classes or facies, it has proven difficult to calibrate the resulting classification with borehole data.
The use of Kohonen self organizing maps in connection with seismic exploration or investigations is also disclosed in U.S. Pat. Nos. 6,011,557; 5,940,777; 5,862,513; 5,519,805; 5,490,062; 5,373,486 and 5,058,034.
A need continues to exist, however, for an improved method for utilizing seismic data to estimate lithological characteristics of the earth's subsurface.
It should be noted that the description of the invention which follows should not be construed as limiting the invention to the examples and preferred embodiments shown and descried. Those skilled in the art to which this invention pertains will be able to devise variations of this invention within the scope of the appended claims.